Autonomous Environment Monitoring Using IoT for Indoor Data Collection via AMR

Abstract

This paper describes a system for gathering indoor environmental data autonomously using an Autonomous Mobile 
Robot (AMR). The robot carries IoT-enabled sensors that measure temperature, humidity and air quality, and 
navigates indoor spaces to collect readings and transmit them to a central server for analysis and visualization. Unlike 
a fixed sensor network, which measures only the points where sensors happen to be installed, a mobile platform 
samples the whole space, so a problem confined to one corner of a room is not missed. The robot is built on a 
Raspberry Pi with a LiDAR sensor for navigation, a camera for perception, BO motors driven through an L298N 
motor driver, and a Li-ion battery. Environmental sensing uses a DHT-11 for temperature and humidity, an MQ-135 
for general air quality including CO2, and an MQ-7 for carbon monoxide. Readings are uploaded to an Adafruit IO 
dashboard, where they are viewed as live feeds and line charts. The software stack is built on the Robot Operating 
System (ROS), which supplies hardware abstraction, message passing between nodes and visualization tools. The 
system provides real-time insight into indoor conditions, supporting decisions about ventilation, energy use and 
occupant safety.